An Optimized Parallel Decision Tree Model Based on Rough Set Theory

2008 ◽  
pp. 832-839
Author(s):  
Xiaowang Ye ◽  
Zhijing Liu
Keyword(s):  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Tree Model ◽  
Tree Model ◽  
Model Based

Cost-effectiveness of rabies post-exposure prophylaxis in the context of very low rabies risk: A decision-tree model based on the experience of France

Vaccine ◽  
2015 ◽  
Vol 33 (20) ◽  
pp. 2367-2378 ◽  
Author(s):  
Florence Ribadeau Dumas ◽  
Dieynaba S. N’Diaye ◽  
Juliette Paireau ◽  
Philippe Gautret ◽  
Hervé Bourhy ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Post Exposure Prophylaxis ◽  
Tree Model ◽  
Post Exposure ◽  
Model Based ◽  
Exposure Prophylaxis

scholarly journals Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

2019 ◽  
Vol 11 (17) ◽  
pp. 4513 ◽  
Author(s):  
Xiaoqing Li ◽  
Qingquan Jiang ◽  
Maxwell K. Hsu ◽  
Qinglan Chen
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Assessment Model ◽  
Software Project ◽  
Risk Assessment Model ◽  
Backpropagation Neural Network ◽  
Model Based ◽  
Sample Data

Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from the original sample dataset. The input factors of the software project risk assessment model could be reduced from thirty-five to twelve by the attribute reduction. Finally, the refined sample data subset was used to train the BPNN and the test sample data subset was used to verify the trained BPNN. The test results showed that the proposed joint model could achieve a better assessment than the model based only on the BPNN.

Sign in / Sign up

Export Citation Format

Share Document